WO2023109652A1 - 水稻种植提取及复种指数监测方法、系统、终端及存储介质 - Google Patents

水稻种植提取及复种指数监测方法、系统、终端及存储介质 Download PDF

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WO2023109652A1
WO2023109652A1 PCT/CN2022/137663 CN2022137663W WO2023109652A1 WO 2023109652 A1 WO2023109652 A1 WO 2023109652A1 CN 2022137663 W CN2022137663 W CN 2022137663W WO 2023109652 A1 WO2023109652 A1 WO 2023109652A1
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ndvi
rice
layer
time series
rice planting
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French (fr)
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李洪忠
韩宇
赵龙龙
李晓丽
姜小砾
陈劲松
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Shenzhen Institute of Advanced Technology of CAS
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G22/00—Cultivation of specific crops or plants not otherwise provided for
    • A01G22/20—Cereals
    • A01G22/22—Rice
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G06T2207/10036—Multispectral image; Hyperspectral image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20032—Median filtering
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30192—Weather; Meteorology

Definitions

  • the application belongs to the technical field of agricultural data processing, and specifically relates to a rice planting extraction and multiple cropping index monitoring method, system, terminal and storage medium.
  • Rice is one of the main food crops in the world. Tropical and subtropical regions (such as South Asia and Southeast Asia) are one of the main rice production areas in the world. Suitable climatic conditions and abundant water resources make intensive production of rice and planting of various crops possible.
  • the multi-cropping index of rice refers to the ratio of the total sown area of rice to the paddy field area in a year, and is an important indicator reflecting the rice planting mode. Remote sensing is the main means of monitoring rice planting conditions in a large area, and it has unique advantages in monitoring rice planting distribution and rice multiple cropping index.
  • Rice cultivation has obvious phenological characteristics, and researchers at home and abroad mainly identify and extract rice based on the phenological characteristics of rice.
  • the phenological period of rice mainly includes seven stages: sowing and seedling raising stage, transplanting and turning green stage, tillering stage, booting and heading stage, milk ripening stage and maturity stage.
  • the growth cycle of rice in different regions and different ripening methods is also different.
  • the growth cycle of early rice in tropical areas is about 90 days, while that of late rice is about 150 days.
  • NDVI Normalized Difference Vegetation Index, Normalized Difference Vegetation Index
  • EVI Enhanced Vegetation Index, Enhanced Vegetation Index
  • LSWI Land Surface Water Index, Surface Moisture Index
  • the paddy field needs to be irrigated a lot to maintain an appropriate depth of water layer.
  • the water index LSWI is slightly higher than the vegetation index NDVI (or EVI).
  • NDVI vegetation index
  • the current synthesis method of the median value of the vegetation index NDVI (or EVI) often ignores the trough value, so that the rice booting
  • the difference between the peak value of NDVI (or EVI) at the heading stage and the trough value of NDVI (or EVI) after harvest is small, which does not meet the identification rules of rice; (2) affected by weather, precipitation and irrigation conditions, different years, Rice in different regions and at different sowing dates is at the stage of transplanting and turning green, and the relationship difference between the water index LSWI and the vegetation index NDVI (or EVI) of the rice field is inconsistent, and a single difference threshold setting is difficult to apply to rice identification and mapping in large areas .
  • the existing rice planting identification methods are difficult to apply to large-scale, large-scale, multi-period rice identification and mapping, which further affects the extraction accuracy of rice multiple cropping index.
  • One of the purposes of this application is to provide a rice planting extraction and multiple cropping index monitoring method, comprising the following steps:
  • Step S110 Input the original remote sensing images of time series MOD09GQ and MYD09GQ in the same area;
  • Step S120 Processing the daily MOD09GQ and MYD09GQ images respectively;
  • Step S130 performing median synthesis on the processed images at an interval of 8 days;
  • Step S140 Filtering the images after median synthesis to generate a filtered time series
  • Step S150 The discriminant conditions for rice planting are converted into 6 separate layers, and the 6 layers include: local maximum time series layer Forward NDVI difference discrimination layer Backward NDVI difference discrimination layer Forward Mann-Kendall trend test layer Backward Mann-Kendall trend test layer And forward 40-80 days water coverage discrimination layer
  • Step S160 judge the distribution of rice planting and the multiple cropping index of rice in the time interval according to the 6 layers.
  • step S110 in the step of inputting the original remote sensing images of MOD09GQ and MYD09GQ in the same area, the time range of the original remote sensing images of MOD09GQ and MYD09GQ is from October of the first year to March of the third year , with a total time span of 1.5 years, the MOD09GQ and MYD09GQ remote sensing images include two 250-meter resolution spectral segments, which are named near-infrared spectral segment and red spectral segment.
  • step S120 the step of separately processing the daily MOD09GQ and MYD09GQ images specifically includes:
  • the normalized difference vegetation index data NDVI of the study area is calculated separately for the image after the cloud mask, and the calculation formula is as follows:
  • NDVI o/y (B nir -B red )/(B nir +B red )
  • NDVI o and NDVI y correspond to MOD09GQ and MYD09GQ images, respectively.
  • step S130 performing median synthesis on the processed images at intervals of 8 days, the following steps are specifically included:
  • the median value of the generated NDVI time series is synthesized at an interval of 8 days, and the calculation formula is as follows:
  • d represents the date of synthesis
  • median is the median operator
  • step S140 the step of filtering the median composited image and generating the filtered time series specifically includes the following steps:
  • step S150 in the step of transforming the discriminant conditions of rice planting into 6 separate layers, the following steps are specifically included:
  • the time-series MOD09GA and MYD09GA original remote sensing images of the same area the time range is from October of the first year to March of the third year, and the total time span is 1.5 years.
  • the MOD09GA and MYD09GA remote sensing images include seven 500-meter resolution spectral segments.
  • the daily MOD09GA and MYD09GA images are tested for cloud coverage respectively, and the cloud coverage pixels are masked;
  • the median average is used to calculate the spectral values of the terminal pixels of towns, water bodies, and woodlands, which are respectively marked as
  • Discriminant layer based on water coverage Create a water coverage discrimination layer within 40-80 days ahead Calculated as follows:
  • AND is a bitwise AND operator. if layer A pixel value of 1 means that rice was planted in this pixel on date d, and its NDVI reached the maximum value in the entire phenological period.
  • step S160 in the step of judging the rice planting distribution and the multiple cropping index of rice in the time interval according to the 6 layer layers, the following steps are specifically included:
  • the Layer Paddy represents the multiple cropping index of rice in this pixel, namely
  • the second purpose of this application is to provide a rice planting extraction and multiple cropping index monitoring system, including:
  • Data acquisition unit used to input time series MOD09GQ and MYD09GQ original remote sensing images in the same area;
  • Data processing unit used to process the daily MOD09GQ and MYD09GQ images respectively;
  • Median synthesis unit used to perform median synthesis on the processed image at an interval of 8 days;
  • Filtering unit used to filter the image after median synthesis to generate a filtered time series
  • Discriminant unit the discriminant conditions for rice planting are converted into 6 separate layers, and the 6 layers include: local maximum time series layer Forward NDVI difference discrimination layer Backward NDVI difference discrimination layer Forward Mann-Kendall trend test layer Backward Mann-Kendall trend test layer And forward 40-80 days water coverage discrimination layer
  • Output unit for judging the distribution of rice planting and the multiple cropping index of rice in the time interval according to the 6 layers.
  • the third purpose of the present application is to provide a terminal, including: the terminal includes a processor and a memory coupled to the processor, wherein,
  • the memory stores program instructions for realizing the method for extracting rice planting and multiple cropping index monitoring
  • the processor is used to execute the program instructions stored in the memory to control rice planting extraction and multiple cropping index monitoring.
  • the fourth object of the present application is to provide a storage medium, which stores program instructions executable by a processor, and the program instructions are used to execute the rice planting extraction and multiple cropping index monitoring method.
  • the rice planting extraction and multiple cropping index monitoring method, method, terminal and storage medium provided by this application use the long-term MODIS remote sensing data as the data source, and re-synthesize the time-series 8-day vegetation index (NDVI) data corresponding to the original MOD09GQ and MYD09GQ data.
  • NDVI time-series 8-day vegetation index
  • the phenological characteristics of rice growth based on local maximum detection and Mann-Kendall trend test as the difference discrimination of vegetation index (NDVI) during the key phenological period of rice growth, and the method of identifying surface attributes through daily mixed pixel decomposition to identify rice fields for large-scale irrigation
  • NDVI vegetation index
  • the occurrence of the phenomenon, the above multi-aspect discrimination results correspond to the phenological characteristics of rice, and a rice planting identification rule suitable for cloudy and rainy areas, large-scale, and full-time is designed, which can not only extract the spatial distribution information of tropical and subtropical rice planting , can also extract rice multiple cropping index, which can be used to analyze the temporal and spatial distribution and changes of rice planting patterns, which can overcome the complex and changeable planting patterns, non-uniform planting time, Issues such as lack of key phenological period data.
  • the rice planting extraction and multiple cropping index monitoring method, method, terminal and storage medium provided by this application do not need to distinguish other types of features, such as forest land, cities, water bodies, etc.
  • the threshold value in the identification rules is determined.
  • the data source required by this method is easier to obtain, the amount of data to be processed is small, the classification rules are simple, and the work efficiency is high. It is also applicable to the absence of key phenological periods.
  • Fig. 1 is a flow chart of the steps of the rice planting extraction and multiple cropping index monitoring method provided by the embodiment of the present application.
  • Fig. 2 is a schematic structural diagram of the rice planting extraction and multiple cropping index monitoring method provided in the embodiment of the present application.
  • FIG. 3 is a schematic diagram of a terminal structure provided by an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of a storage medium provided by an embodiment of the present application.
  • Fig. 5 is a schematic diagram of the monitoring results of rice distribution and multiple cropping index in Indochina Peninsula in Southeast Asia and South my country in 2015 provided by the embodiment of the present application.
  • first and second are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, a feature defined as “first” and “second” may explicitly or implicitly include one or more of these features.
  • “plurality” means two or more, unless otherwise specifically defined.
  • Fig. 1 the flow chart of the steps of the rice planting extraction and multiple cropping index monitoring method provided by the application, including the following steps:
  • Step S110 Input the time series MOD09GQ and MYD09GQ original remote sensing images of the same area.
  • the time range of the original remote sensing images of MOD09GQ and MYD09GQ is from October of the first year to March of the third year, with a total time span of 1.5
  • the MOD09GQ and MYD09GQ remote sensing images included two 250-meter resolution spectral segments, which were named near-infrared spectral segment and red spectral segment.
  • Step 120 Process the daily MOD09GQ and MYD09GQ images respectively.
  • the daily MOD09GQ and MYD09GQ images are processed respectively, which specifically includes the following steps: performing cloud cover detection on the daily MOD09GQ and MYD09GQ images respectively, and performing mask processing on the cloud cover pixels;
  • the normalized difference vegetation index data NDVI of the study area were calculated from the images behind the film respectively, and the calculation formula is as follows:
  • NDVI o/y (B nir -B red )/(B nir +B red )
  • NDVI o and NDVI y correspond to MOD09GQ and MYD09GQ images, respectively.
  • Step S130 Carry out median synthesis on the processed images at an interval of 8 days.
  • the median value of the generated NDVI time series is synthesized at an interval of 8 days, and the calculation formula is as follows:
  • d represents the date of synthesis
  • median is the median operator
  • the selection of a large number of pure pixels and the median average processing eliminate the influence of cloud ginseng coverage and ensure the representativeness of terminal pixels.
  • the judgment of the irrigation phenomenon during the greening period of rice transplanting is mainly based on the difference between the vegetation index NDVI (or EVI) and the surface moisture index (LSWI), but the difference threshold is affected by weather, precipitation, region, planting, etc. Therefore, it is necessary to conduct statistical analysis separately for specific study areas to determine.
  • This application applies the method of daily mixed pixel decomposition, and separates MOD09GA and MYD09GA data to avoid the influence of cloud coverage to the greatest extent.
  • the irrigation phenomenon it is judged based on the relative size relationship of the mixed pixel decomposition value. It is necessary to obtain the experience threshold in advance, which can be applied to large-scale, multi-temporal, and perennial applications.
  • Step S140 Filter the median-composited image to generate a filtered time series.
  • the step of filtering the image after median synthesis and generating the filtered time series specifically includes the following steps:
  • the original method is only based on the Savitzky-Golay filtered time series
  • the Savitzky-Golay filter will be overfitted, and there is a certain probability of error in the interpolation of the missing phase data, that is, the interpolated data is far from the actual situation, resulting in the maximum value of the vegetation index NDVI during the rice growth period.
  • the point detection a certain proportion of error points occurs, so in the original method, the non-cultivated land feature category needs to be eliminated first; this embodiment can be directly applied to all ground feature categories, and will meet the requirements of rice growth period vegetation.
  • Step S150 The discriminant conditions for rice planting are converted into 6 separate layers, and the 6 layers include: local maximum time series layer Forward NDVI difference discrimination layer Backward NDVI difference discrimination layer Forward Mann-Kendall trend test layer Backward Mann-Kendall trend test layer And forward 40-80 days water coverage discrimination layer
  • the discriminant conditions for rice planting are transformed into 6 separate layers, specifically including:
  • the time-series MOD09GA and MYD09GA original remote sensing images of the same area the time range is from October of the first year to March of the third year, and the total time span is 1.5 years.
  • the MOD09GA and MYD09GA remote sensing images include seven 500-meter resolution spectral segments.
  • the daily MOD09GA and MYD09GA images are tested for cloud coverage respectively, and the cloud coverage pixels are masked;
  • the median average is used to calculate the spectral values of the terminal pixels of towns, water bodies, and woodlands, which are respectively marked as
  • Discriminant layer based on water coverage Create a water coverage discrimination layer within 40-80 days ahead Calculated as follows:
  • AND is a bitwise AND operator. if layer A pixel value of 1 means that rice was planted in this pixel on date d, and its NDVI reached the maximum value in the entire phenological period.
  • Step S160 judge the distribution of rice planting and the multiple cropping index of rice in the time interval according to the 6 layers.
  • the Layer Paddy represents the multiple cropping index of rice in this pixel, namely
  • Fig. 2 is a structural schematic diagram of the rice planting extraction and multiple cropping index monitoring system provided by the application, including: data acquisition unit 110: used to input the original remote sensing images of the same area time series MOD09GQ and MYD09GQ; data processing unit 120: used to The MOD09GQ and MYD09GQ images of each day are processed separately; the median synthesis unit 130: used to perform median synthesis on the processed images at an interval of 8 days; the filter unit 140: used to filter the median synthesized image, Generate the filtered time series; discriminant unit 150: the discriminant conditions for rice planting are transformed into 6 separate layers, and the 6 layers include: local maximum value time series layer Forward NDVI difference discrimination layer Backward NDVI difference discrimination layer Forward Mann-Kendall trend test layer Backward Mann-Kendall trend test layer And forward 40-80 days water coverage discrimination layer Output unit 160: for judging the distribution of rice planting and the multiple cropping index of rice in the time interval according to the six layers. Its detailed implementation has been described
  • FIG. 3 is a schematic diagram of a terminal structure in an embodiment of the present application.
  • the terminal 50 includes a processor 51 and a memory 52 coupled to the processor 51 .
  • the memory 52 stores program instructions for realizing the rice planting extraction and multiple cropping index monitoring method.
  • the processor 51 is used to execute the program instructions stored in the memory 52 to control the rice planting extraction and multiple cropping index monitoring.
  • the processor 51 may also be referred to as a CPU (Central Processing Unit, central processing unit).
  • the processor 51 may be an integrated circuit chip with signal processing capability.
  • the processor 51 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components .
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • FIG. 4 is a schematic structural diagram of a storage medium according to an embodiment of the present application.
  • the storage medium of the embodiment of the present application stores a program file 61 capable of realizing all the above-mentioned methods, wherein the program file 61 can be stored in the above-mentioned storage medium in the form of a software product, and includes several instructions to make a computer device (which can It is a personal computer, a server, or a network device, etc.) or a processor (processor) that executes all or part of the steps of the methods in various embodiments of the present application.
  • a computer device which can It is a personal computer, a server, or a network device, etc.
  • processor processor
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc., which can store program codes. , or terminal devices such as computers, servers, mobile phones, and tablets.
  • the rice planting extraction and multiple cropping index monitoring method, method, terminal and storage medium provided by this application use the long-term MODIS remote sensing data as the data source, and re-synthesize the time-series 8-day vegetation index (NDVI) data corresponding to the original MOD09GQ and MYD09GQ data.
  • NDVI time-series 8-day vegetation index
  • the phenological characteristics of rice growth based on local maximum detection and Mann-Kendall trend test as the difference discrimination of vegetation index (NDVI) during the key phenological period of rice growth, and the method of identifying surface attributes through daily mixed pixel decomposition to identify rice fields for large-scale irrigation
  • NDVI vegetation index
  • the occurrence of the phenomenon, the above multi-aspect discrimination results correspond to the phenological characteristics of rice, and a rice planting identification rule suitable for cloudy and rainy areas, large-scale, and full-time is designed, which can not only extract the spatial distribution information of tropical and subtropical rice planting , can also extract rice multiple cropping index, which can be used to analyze the temporal and spatial distribution and changes of rice planting patterns, which can overcome the complex and changeable planting patterns, non-uniform planting time, Issues such as lack of key phenological period data.
  • the rice planting extraction and multiple cropping index monitoring method, method, terminal and storage medium provided by this application do not need to distinguish other types of features, such as forest land, cities, water bodies, etc.
  • the threshold value in the identification rules is determined.
  • the data source required by this method is easier to obtain, the amount of data to be processed is small, the classification rules are simple, and the work efficiency is high. It is also applicable to the absence of key phenological periods.
  • Figure 5 shows the monitoring results of rice distribution and multiple cropping index in Indochina Peninsula of Southeast Asia and South my country in 2015 based on the application scheme.
  • Indochina Peninsula includes Sri Lanka, Cambodia, Laos, and Vietnam, and South my country includes Hainan, Guangxi, Guangdong, and Fujian.
  • the entire region ranges from the tropics in the south to the subtropics in the north, and the rice planting pattern also ranges from three-crop rice in the south to two-crop rice in the north.
  • the rice planting pattern is one-crop rice.
  • Figure 5 highlights the main rice producing areas in the study area, such as the Ayeyarwady River Delta, the Mekong River Plain, the Korat Plateau, the Mekong River Delta, the Red River Delta, the Nandu River in Leizhou, and the coast of Jiangmen.
  • the accuracy test was carried out by randomly arranging sample points, the rice distribution accuracy was higher than 90%, and the multiple cropping index accuracy was higher than 85%.

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Abstract

本申请提供的水稻种植提取及复种指数监测方法、方法、终端及存储介质，利用长时间序列的MODIS遥感数据作为数据源，基于原始MOD09GQ与MYD09GQ数据重新合成时序8天植被指数数据对应水稻生长的物候特征，基于局部最大值检测及Mann-Kendall趋势检验作为水稻生长关键物候期间的植被指数差异判别，并通过逐日混合像元分解辨识地表属性的方法判别水稻田进行大量灌溉现象的发生，将以上的多方面判别结果与水稻物候特征规律相对应，设计一种适用于多云多雨地区、大范围、全时期的水稻种植识别规则，不仅可以提取热带、亚热带水稻种植空间分布信息，还能提取水稻复种指数，用来分析水稻种植模式的时空分布及变化。

Description

水稻种植提取及复种指数监测方法、系统、终端及存储介质 技术领域
本申请属于农业数据处理技术领域，具体涉及一种水稻种植提取及复种指数监测方法、系统、终端及存储介质。
背景技术
水稻是全世界主要的粮食作物之一。热带、亚热带地区(如南亚、东南亚)是全球水稻的主要生产区之一，适宜的气候条件和丰富的水资源使水稻的集约生产和多种作物的种植成为可能。水稻复种指数是指全年内水稻的总播种面积与水田面积之比，是反映水稻种植模式的重要指标。遥感是大范围水稻种植状况监测的主要手段，在监测水稻种植分布及水稻复种指数上具有独特的优势。然而，在热带、亚热带水稻主产区，遥感监测水稻仍面临三个主要问题：(1)水稻产区多云雨天气，严重影响水稻生长期连续光学影像的获取；(2)水稻产区水热资源丰富，使得水稻的种植模式非常复杂，不同区域，水稻的种植时间、生长周期以及复种指数呈现较大差异性；(3)受人类活动与气候变化的双重影响，水稻的种植模式更加复杂多变，同一区域内可能存在较大差异。这些因素降低了遥感监测水稻的稳定性、精度以及大范围的适用性。
水稻种植具有明显的物候特征，国内外的研究人员主要基于水稻的物候特征来进行水稻的识别与提取。水稻的物候期主要包括七个阶段：播种育秧期、移栽返青期、分蘖期、孕穗抽穗期、乳熟期以及成熟期。不同地区、不同熟制的水稻，其生长周期也有所差别，热带地区早稻生长周期90天左右，而晚稻则为150天左右。
光学影像的遥感指数，如NDVI(Normalized Difference Vegetation Index,归 一化植被指数)、EVI(Enhanced Vegetation Index，增强型植被指数)、LSWI(Land Surface Water Index，地表水分指数)等，其时序的变化与水稻的形态特征、生化参量以及物候信息有着较好的相关关系。NDVI(或EVI)在水稻移栽返青期较低，随着叶片与植株的生长逐渐增加，至孕穗抽穗期，NDVI(或EVI)达到高峰最大值，然后随着水稻的逐渐成熟，NDVI(或EVI)逐渐降低。而在水稻移栽返青期，由于水稻的生物学特性，水稻田需进行大量灌溉以保持适当深度的水层，在此期间，水分指数LSWI略高于植被指数NDVI(或EVI)。综合植被指数高峰最大值与最小值的差异、时间间隔以及移栽返青期水分指数与植被指数的关系，是目前水稻识别与提取的最常用方法。
在热带、亚热带区域，水稻主产区多云雨天气，往往导致关键物候期数据的缺失，进而使得水稻识别规则的无效，对于现有技术来说，主要存在两方面的影响：(1)水稻收割后，杂草与水稻稻茬生长迅速，植被指数NDVI(或EVI)的低谷值持续时间较短，目前的植被指数NDVI(或EVI)中值合成方式往往忽略到其低谷值，从而使得水稻孕穗抽穗期NDVI(或EVI)高峰最大值与收割后NDVI(或EVI)低谷值的差值较小，使得不满足水稻的识别规则；(2)受天气、降水以及灌溉条件的影响，不同年份、不同地区、不同播种期的水稻在移栽返青期，水稻田的水分指数LSWI与植被指数NDVI(或EVI)的关系差值不一致，单一的差值阈值设置难以适用于大区域的水稻识别与制图。总体而言，受水稻产区多云多雨天气的影响，现有的水稻种植识别方法难以适用于大规模、大范围、多时期的水稻识别与制图，并进而影响水稻复种指数的提取精度。
发明内容
鉴于此，有必要针对现有技术存在的缺陷提供一种能够高效、准确地实现大范围的水稻种植制图以及水稻复种指数制图的水稻种植提取及复种指数监测方法、系统、终端以及存储介质。
为解决上述问题，本申请采用下述技术方案：
本申请目的之一，在于提供一种水稻种植提取及复种指数监测方法，包括下述步骤：
步骤S110：输入同一地区时序MOD09GQ与MYD09GQ原始遥感影像；
步骤S120：对每天的MOD09GQ与MYD09GQ影像分别进行处理；
步骤S130：以8天为时间间隔对处理后的影像进行中值合成；
步骤S140：对中值合成后的影像进行滤波，生成滤波后的时间序列；
步骤S150：对水稻种植的判别条件转变为单独的6个图层，所述6个图层包括：局部极大值时间序列图层
Figure PCTCN2022137663-appb-000001
前向NDVI差异判别图层
Figure PCTCN2022137663-appb-000002
后向NDVI差异判别图层
Figure PCTCN2022137663-appb-000003
前向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000004
后向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000005
及前向40-80天内水覆盖判别图层
Figure PCTCN2022137663-appb-000006
步骤S160：根据所述6个图层图层判断该时间区间内的水稻种植分布以及水稻的复种指数。
在其中一些实施例中，在步骤S110，输入同一地区时序MOD09GQ与MYD09GQ原始遥感影像的步骤中，所述MOD09GQ与MYD09GQ原始遥感影像的时间范围从第一年的10月份至第三年的3月份，时间总跨度1.5年，所述MOD09GQ与MYD09GQ遥感影像包括2个250米分辨率谱段，将这2个谱段命名为近红外谱段及红谱段。
在其中一些实施例中，在步骤S120，对每天的MOD09GQ与MYD09GQ影像分别进行处理的步骤中，具体包括：
对每天的MOD09GQ与MYD09GQ影像分别进行云覆盖检测，并将云覆盖像元进行掩膜处理；
对云掩膜后的影像分别计算研究区域的归一化差分植被指数数据NDVI，计算公式如下：
NDVI o/y＝(B nir-B red)/(B nir+B red)
NDVI o、NDVI y分别对应MOD09GQ与MYD09GQ影像。
在其中一些实施例中，在步骤S130：以8天为时间间隔对处理后的影像进行中值合成的步骤中，具体包括下述步骤：
以8天为时间间隔对生成的NDVI时序进行中值合成，计算公式如下：
Figure PCTCN2022137663-appb-000007
其中，d表示合成的日期，d-3≤d 1,d 2≤d+4，median为中值算子。
在其中一些实施例中，在步骤S140，对中值合成后的影像进行滤波，生成滤波后的时间序列的步骤中，具体包括下述步骤：
对生成的时间序列NDVI d进行时间窗口为32，即{NDVI d+i*8}i＝-2,-1,0,1,2.的Savitzky-Golay滤波，生成滤波后的时间序列
Figure PCTCN2022137663-appb-000008
在其中一些实施例中，在步骤S150：对水稻种植的判别条件转变为单独的6个图层的步骤中，具体包括下述步骤：
对滤波后的时间序列
Figure PCTCN2022137663-appb-000009
进行时间窗口50的局部极大值检测，按照
Figure PCTCN2022137663-appb-000010
的序列时间建立局部极大值时间序列图层
Figure PCTCN2022137663-appb-000011
计算公式如下：
Figure PCTCN2022137663-appb-000012
对原始的时间序列NDVI d进行前向40天，即前向6个时相{NDVI d-i*8}i＝5,4,3,2,1,0.的Mann-Kendall趋势检验，按照NDVI d的序列时间建立前向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000013
计算公式如下：
FMK＝Mann-Kendall{NDVI d-i*8},i＝5,4,3,2,1,0.
Figure PCTCN2022137663-appb-000014
对原始的时间序列NDVI d进行后向40天，即后向6个时相{NDVI d+i*8}i＝0,1,2,3,4,5.的Mann-Kendall趋势检验，按照NDVI d的序列时间建立后向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000015
计算公式如下：
BMK＝Mann-Kendall{NDVI d+i*8},i＝0,1,2,3,4,5.
Figure PCTCN2022137663-appb-000016
对滤波后的时间序列
Figure PCTCN2022137663-appb-000017
进行前向30-70天内的最小值计算及前向差异足够判别，建立前向NDVI差异判别图层
Figure PCTCN2022137663-appb-000018
计算公式如下：
Figure PCTCN2022137663-appb-000019
Figure PCTCN2022137663-appb-000020
对滤波后的时间序列
Figure PCTCN2022137663-appb-000021
进行后向30-70天内的最小值计算及后向差异足够判别，建立后向NDVI差异判别图层
Figure PCTCN2022137663-appb-000022
计算公式如下：
Figure PCTCN2022137663-appb-000023
Figure PCTCN2022137663-appb-000024
输入同一地区时序MOD09GA与MYD09GA原始遥感影像，时间范围从第一年的10月份至第三年的3月份，时间总跨度1.5年，MOD09GA与MYD09GA遥感影像包括7个500米分辨率谱段，对每天的MOD09GA与MYD09GA影像分别进行云覆盖检测，并将云覆盖像元进行掩膜处理；
基于每天的MOD09GA与MYD09GA云掩膜数据，以及选择的纯净像元，应用中值平均计算城镇、水体、林地终端像元光谱值，分别标记为
Figure PCTCN2022137663-appb-000025
Figure PCTCN2022137663-appb-000026
对MODIS MOD09GA与MYD09GA云掩膜数据，基于上步中的终端像元光谱值，进行混合像元分解Unmix，并建立水覆盖判别图层
Figure PCTCN2022137663-appb-000027
计算公式如下：
Figure PCTCN2022137663-appb-000028
Figure PCTCN2022137663-appb-000029
Figure PCTCN2022137663-appb-000030
基于水覆盖判别图层
Figure PCTCN2022137663-appb-000031
建立前向40-80天内水覆盖判别图层
Figure PCTCN2022137663-appb-000032
计算公式如下：
Figure PCTCN2022137663-appb-000033
基于上面各步骤中生成的局部极大值时间序列图层
Figure PCTCN2022137663-appb-000034
前向NDVI差异判别图层
Figure PCTCN2022137663-appb-000035
后向NDVI差异判别图层
Figure PCTCN2022137663-appb-000036
前向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000037
后向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000038
以及前向40-80天内水覆盖判别图层
Figure PCTCN2022137663-appb-000039
建立水稻种植判别图层
Figure PCTCN2022137663-appb-000040
计算公式如下：
Figure PCTCN2022137663-appb-000041
其中AND为按位与运算符。若图层
Figure PCTCN2022137663-appb-000042
像元值为1，则表示在日期d，该像元内种植水稻，且其NDVI达到整个物候期内的最大值。
在其中一些实施例中，在步骤S160：根据所述6个图层图层判断该时间区间内的水稻种植分布以及水稻的复种指数的步骤中，具体包括下述步骤：
根据图层
Figure PCTCN2022137663-appb-000043
设定日期区间的上下限进行累加，可以判断该时间区间内的水稻种植分布，如下式所示：
Figure PCTCN2022137663-appb-000044
Figure PCTCN2022137663-appb-000045
若日期的区间设定为全年，则图层Layer Paddy表示该像元水稻的复种指数，即
Figure PCTCN2022137663-appb-000046
本申请目的之二，在于提供一种水稻种植提取及复种指数监测系统，包括：
数据采集单元：用于输入同一地区时序MOD09GQ与MYD09GQ原始遥感影像；
数据处理单元：用于对每天的MOD09GQ与MYD09GQ影像分别进行处理；
中值合成单元：用于以8天为时间间隔对处理后的影像进行中值合成；
滤波单元：用于对中值合成后的影像进行滤波，生成滤波后的时间序列；
判别单元：用于对水稻种植的判别条件转变为单独的6个图层，所述6个图层包括：局部极大值时间序列图层
Figure PCTCN2022137663-appb-000047
前向NDVI差异判别图层
Figure PCTCN2022137663-appb-000048
后向NDVI差异判别图层
Figure PCTCN2022137663-appb-000049
前向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000050
后向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000051
及前向40-80天内水覆盖判别图层
Figure PCTCN2022137663-appb-000052
输出单元：用于根据所述6个图层图层判断该时间区间内的水稻种植分布以及水稻的复种指数。
本申请目的之三，在于提一种终端，包括：所述终端包括处理器、与所述处理器耦接的存储器，其中，
所述存储器存储有用于实现所述的水稻种植提取及复种指数监测方法的程序指令；
所述处理器用于执行所述存储器存储的所述程序指令以控制水稻种植提取及复种指数监测。
本申请目的之四，在于提供一种存储介质，存储有处理器可运行的程序指令，所述程序指令用于执行所述水稻种植提取及复种指数监测方法。
本申请采用上述技术方案具备下述效果：
本申请提供的水稻种植提取及复种指数监测方法、方法、终端及存储介质，利用长时间序列的MODIS遥感数据作为数据源，基于原始MOD09GQ与MYD09GQ数据重新合成时序8天植被指数(NDVI)数据对应水稻生长的物候特征，基于局部最大值检测及Mann-Kendall趋势检验作为水稻生长关键物候期间的植被指数(NDVI)差异判别，并通过逐日混合像元分解辨识地表属性的方法判别水稻田进行大量灌溉现象的发生，将以上的多方面判别结果与水稻物候特征规律相对应，设计一种适用于多云多雨地区、大范围、全时期的水稻种植识别规则，不仅可以提取热带、亚热带水稻种植空间分布信息，还能提取水稻复种指数，用来分析水稻种植模式的时空分布及变化，可克服针对现有光学遥感水稻监测相关技术对于热带、亚热带多云多雨地区的种植模式复杂多变、种植时间不统一、关键物候期数据缺失等问题。
此外，本申请提供的水稻种植提取及复种指数监测方法、方法、终端及存储介质，不用进行其他地物类别，如林地、城市、水体等的区分，而且不需要针对不同种植期的水稻分别设定识别规则中的阈值，该方法所需数据源获取较易，需要处理的数据量较小，分类规则简单，工作效率较高，对关键物候期缺失的情况同样适用。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对本申请实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面所描述的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例提供的水稻种植提取及复种指数监测方法的步骤流程图。
图2是本申请实施例提供的水稻种植提取及复种指数监测方法的结构示意图。
图3为本申请实施例提供的终端结构示意图。
图4为本申请实施例提供的存储介质的结构示意图。
图5为本申请实施例提供的东南亚中南半岛及我国华南地区2015年水稻分布及复种指数监测结果示意图。
具体实施方式
下面详细描述本申请的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本申请，而不能理解为对本申请的限制。
在本申请的描述中，需要理解的是，术语“上”、“下”、“水平”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本申请和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本申请的限制。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多个该特征。在本申请的描述中，“多个”的含义是两个或两个以上，除非另有明确具体的限定。
请参阅图1，为本申请提供的水稻种植提取及复种指数监测方法的步骤流程图，包括下述步骤：
步骤S110：输入同一地区时序MOD09GQ与MYD09GQ原始遥感影像。
在本实施例中，输入同一地区时序MOD09GQ与MYD09GQ原始遥感影像的步骤中，所述MOD09GQ与MYD09GQ原始遥感影像的时间范围从第一年的10月份至第三年的3月份，时间总跨度1.5年，所述MOD09GQ与MYD09GQ遥感影像包括2个250米分辨率谱段，将这2个谱段命名为近红外谱段及红谱段。
步骤120：对每天的MOD09GQ与MYD09GQ影像分别进行处理。
在本实施例中，对每天的MOD09GQ与MYD09GQ影像分别进行处理，具体包括下述步骤：对每天的MOD09GQ与MYD09GQ影像分别进行云覆盖检测，并将云覆盖像元进行掩膜处理；对云掩膜后的影像分别计算研究区域的归一化差分植被指数数据NDVI，计算公式如下：
NDVI o/y＝(B nir-B red)/(B nir+B red)
NDVI o、NDVI y分别对应MOD09GQ与MYD09GQ影像。
步骤S130：以8天为时间间隔对处理后的影像进行中值合成。
在本实施例中，以8天为时间间隔对生成的NDVI时序进行中值合成，计算公式如下：
Figure PCTCN2022137663-appb-000053
其中，d表示合成的日期，d-3≤d 1,d 2≤d+4，median为中值算子。
在本实施例中，大量纯净像元的选取及中值平均处理消除云参覆盖的影响，确保终端像元的代表性。
可以理解，原始的方法中对水稻移栽返青期灌水现象发生的判断主要基于植被指数NDVI(或EVI)与地表水分指数(LSWI)的差值，但差值阈值受到天气、降水、区域、种植期的影响而变动，因此需要对特定研究区分别进行统计分析以确定。本申请应用逐日混合像元分解的方法，并且对MOD09GA与MYD09GA数据分别进行，最大限度的避免云覆盖的影响，而在灌水现象判断中，基于混合像元分解值的相对大小关系进行判断，不需要进行经验阈值的预先获取，能够适用于大范围、多时相、常年的应用。
步骤S140：对中值合成后的影像进行滤波，生成滤波后的时间序列。
在本实施例中，对中值合成后的影像进行滤波，生成滤波后的时间序列的步骤中，具体包括下述步骤：
对生成的时间序列NDVI d进行时间窗口为32，即{NDVI d+i*8}i＝ -2,-1,0,1,2.的Savitzky-Golay滤波，生成滤波后的时间序列
Figure PCTCN2022137663-appb-000054
可以理解，原始的方法仅基于Savitzky-Golay滤波后时间序列
Figure PCTCN2022137663-appb-000055
但是Savitzky-Golay滤波会出现过度拟合，以及对缺失时相数据的插补存在一定概率的错误现象，即插补的数据与实际状况差距较大，从而导致在水稻生长期植被指数NDVI最大值点检测中，出现一定比例的错误点，因此在原始方法中，需首先对非耕地类的地物类别进行剔除；本实施例则可以直接应用到所有地物类别中，将满足水稻生长期植被指数NDVI最大值点的判断分成了5部分，综合应用了原始的时间序列NDVI d和Savitzky-Golay滤波后时间序列
Figure PCTCN2022137663-appb-000056
其中基于Mann-Kendall的前、后向趋势检验能够在部分时相数据缺失的情况下仍有效。
步骤S150：对水稻种植的判别条件转变为单独的6个图层，所述6个图层包括：局部极大值时间序列图层
Figure PCTCN2022137663-appb-000057
前向NDVI差异判别图层
Figure PCTCN2022137663-appb-000058
后向NDVI差异判别图层
Figure PCTCN2022137663-appb-000059
前向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000060
后向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000061
及前向40-80天内水覆盖判别图层
Figure PCTCN2022137663-appb-000062
在本实施例中，对水稻种植的判别条件转变为单独的6个图层，具体包括：
对滤波后的时间序列
Figure PCTCN2022137663-appb-000063
进行时间窗口50的局部极大值检测，按照
Figure PCTCN2022137663-appb-000064
的序列时间建立局部极大值时间序列图层
Figure PCTCN2022137663-appb-000065
计算公式如下：
Figure PCTCN2022137663-appb-000066
对原始的时间序列NDVI d进行前向40天，即前向6个时相{NDVI d-i*8}i＝5,4,3,2,1,0.的Mann-Kendall趋势检验，按照NDVI d的序列时间建立前向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000067
计算公式如下：
FMK＝Mann-Kendall{NDVI d-i*8},i＝5,4,3,2,1,0.
Figure PCTCN2022137663-appb-000068
对原始的时间序列NDVI d进行后向40天，即后向6个时相{NDVI d+i*8}i＝0,1,2,3,4,5.的Mann-Kendall趋势检验，按照NDVI d的序列时间建立后向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000069
计算公式如下：
BMK＝Mann-Kendall{NDVI d+i*8},i＝0,1,2,3,4,5.
Figure PCTCN2022137663-appb-000070
对滤波后的时间序列
Figure PCTCN2022137663-appb-000071
进行前向30-70天内的最小值计算及前向差异足够判别，建立前向NDVI差异判别图层
Figure PCTCN2022137663-appb-000072
计算公式如下：
Figure PCTCN2022137663-appb-000073
Figure PCTCN2022137663-appb-000074
对滤波后的时间序列
Figure PCTCN2022137663-appb-000075
进行后向30-70天内的最小值计算及后向差异足够判别，建立后向NDVI差异判别图层
Figure PCTCN2022137663-appb-000076
计算公式如下：
Figure PCTCN2022137663-appb-000077
Figure PCTCN2022137663-appb-000078
输入同一地区时序MOD09GA与MYD09GA原始遥感影像，时间范围从第一年的10月份至第三年的3月份，时间总跨度1.5年，MOD09GA与MYD09GA遥感影像包括7个500米分辨率谱段，对每天的MOD09GA与MYD09GA影像分别进行云覆盖检测，并将云覆盖像元进行掩膜处理；
基于每天的MOD09GA与MYD09GA云掩膜数据，以及选择的纯净像元，应用中值平均计算城镇、水体、林地终端像元光谱值，分别标记为
Figure PCTCN2022137663-appb-000079
Figure PCTCN2022137663-appb-000080
对MODIS MOD09GA与MYD09GA云掩膜数据，基于上步中的终端像元光谱值，进行混合像元分解Unmix，并建立水覆盖判别图层
Figure PCTCN2022137663-appb-000081
计算公式如下：
Figure PCTCN2022137663-appb-000082
Figure PCTCN2022137663-appb-000083
Figure PCTCN2022137663-appb-000084
基于水覆盖判别图层
Figure PCTCN2022137663-appb-000085
建立前向40-80天内水覆盖判别图层
Figure PCTCN2022137663-appb-000086
计算公式如下：
Figure PCTCN2022137663-appb-000087
基于上面各步骤中生成的局部极大值时间序列图层
Figure PCTCN2022137663-appb-000088
前向NDVI差异判别图层
Figure PCTCN2022137663-appb-000089
后向NDVI差异判别图层
Figure PCTCN2022137663-appb-000090
前向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000091
后向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000092
以及前向40-80天内水覆盖判别图层
Figure PCTCN2022137663-appb-000093
建立水稻种植判别图层
Figure PCTCN2022137663-appb-000094
计算公式如下：
Figure PCTCN2022137663-appb-000095
其中AND为按位与运算符。若图层
Figure PCTCN2022137663-appb-000096
像元值为1，则表示在日期d，该像元内种植水稻，且其NDVI达到整个物候期内的最大值。
步骤S160：根据所述6个图层图层判断该时间区间内的水稻种植分布以及水稻的复种指数。
在本实施例中，根据所述6个图层图层判断该时间区间内的水稻种植分布以及水稻的复种指数的步骤中，具体包括下述步骤：
根据图层
Figure PCTCN2022137663-appb-000097
设定日期区间的上下限进行累加，可以判断该时间区间内的水稻种植分布，如下式所示：
Figure PCTCN2022137663-appb-000098
Figure PCTCN2022137663-appb-000099
若日期的区间设定为全年，则图层Layer Paddy表示该像元水稻的复种指数，即
Figure PCTCN2022137663-appb-000100
请参阅图2，为本申请提供的水稻种植提取及复种指数监测系统的结构示意图，包括：数据采集单元110：用于输入同一地区时序MOD09GQ与MYD09GQ原始遥感影像；数据处理单元120：用于对每天的MOD09GQ与MYD09GQ影像分别进行处理；中值合成单元130：用于以8天为时间间隔对处理后的影像进行中值合成；滤波单元140：用于对中值合成后的影像进行滤波，生成滤波后的时间序列；判别单元150：用于对水稻种植的判别条件转变为单独的6个图层，所述6个图层包括：局部极大值时间序列图层
Figure PCTCN2022137663-appb-000101
前向NDVI差异判别图层
Figure PCTCN2022137663-appb-000102
后向NDVI差异判别图层
Figure PCTCN2022137663-appb-000103
前向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000104
后向Mann-Kendall趋势检验图层
Figure PCTCN2022137663-appb-000105
及前向40-80天内水覆盖判别图层
Figure PCTCN2022137663-appb-000106
输出单元160：用于根据所述6个图层图层判断该时间区间内的水稻种植分布以及水稻的复种指数。其详细的实现方式在本申请上述方法描述中已经说明，这里不再赘述。
请参阅图3，为本申请实施例的终端结构示意图。该终端50包括处理器51、与处理器51耦接的存储器52。
存储器52存储有用于实现所述的水稻种植提取及复种指数监测方法的程序指令。
处理器51用于执行存储器52存储的程序指令以控制所述的水稻种植提取及复种指数监测。
其中，处理器51还可以称为CPU(Central Processing Unit，中央处理单元)。处理器51可能是一种集成电路芯片，具有信号的处理能力。处理器51还可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程 门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
请参阅图4，为本申请实施例的存储介质的结构示意图。本申请实施例的存储介质存储有能够实现上述所有方法的程序文件61，其中，该程序文件61可以以软件产品的形式存储在上述存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(processor)执行本申请各个实施方式方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质，或者是计算机、服务器、手机、平板等终端设备。
本申请提供的水稻种植提取及复种指数监测方法、方法、终端及存储介质，利用长时间序列的MODIS遥感数据作为数据源，基于原始MOD09GQ与MYD09GQ数据重新合成时序8天植被指数(NDVI)数据对应水稻生长的物候特征，基于局部最大值检测及Mann-Kendall趋势检验作为水稻生长关键物候期间的植被指数(NDVI)差异判别，并通过逐日混合像元分解辨识地表属性的方法判别水稻田进行大量灌溉现象的发生，将以上的多方面判别结果与水稻物候特征规律相对应，设计一种适用于多云多雨地区、大范围、全时期的水稻种植识别规则，不仅可以提取热带、亚热带水稻种植空间分布信息，还能提取水稻复种指数，用来分析水稻种植模式的时空分布及变化，可克服针对现有光学遥感水稻监测相关技术对于热带、亚热带多云多雨地区的种植模式复杂多变、种植时间不统一、关键物候期数据缺失等问题。
此外，本申请提供的水稻种植提取及复种指数监测方法、方法、终端及存储介质，不用进行其他地物类别，如林地、城市、水体等的区分，而且不需要针对不同种植期的水稻分别设定识别规则中的阈值，该方法所需数据源获取较易，需要处理的数据量较小，分类规则简单，工作效率较高，对关键物候期缺失的情况同样适用。
请参阅图5，为基于本申请方案的东南亚中南半岛及我国华南地区2015年水稻分布及复种指数监测结果。中南半岛包括缅甸、泰国、柬埔寨、老挝、越南，我国华南地区包括海南、广西、广东、福建。整个区域从最南端的热带至北部的亚热带，水稻的种植模式也从最南端的三季稻至北部的二季稻，部分区域由于经济作物种植、地形、降水等影响，水稻种植模式为一季稻。图5中将研究区中的主要水稻产区，如伊洛瓦底江三角洲、湄公河平原、呵叻高原、湄公河三角洲、红河三角洲、雷州南渡河、江门沿海等区域重点展示。实验结果与中南半岛各国、我国各省统计资料对比，具有很高的一致性。通过随机布设样点方式进行精度检验，水稻分布精度高于90％，复种指数精度高于85％。
以上仅为本申请的实施例而已，并不用于限制本申请。对于本领域技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在本申请的权利要求范围之内。

Claims (10)

  1. 一种水稻种植提取及复种指数监测方法，其特征在于，包括下述步骤：
    步骤S110：输入同一地区时序MOD09GQ与MYD09GQ原始遥感影像；
    步骤S120：对每天的MOD09GQ与MYD09GQ影像分别进行处理；
    步骤S130：以8天为时间间隔对处理后的影像进行中值合成；
    步骤S140：对中值合成后的影像进行滤波，生成滤波后的时间序列；
    步骤S150：对水稻种植的判别条件转变为单独的6个图层，所述6个图层包括：局部极大值时间序列图层
    Figure PCTCN2022137663-appb-100001
    前向NDVI差异判别图层
    Figure PCTCN2022137663-appb-100002
    后向NDVI差异判别图层
    Figure PCTCN2022137663-appb-100003
    前向Mann-Kendall趋势检验图层
    Figure PCTCN2022137663-appb-100004
    后向Mann-Kendall趋势检验图层
    Figure PCTCN2022137663-appb-100005
    及前向40-80天内水覆盖判别图层
    Figure PCTCN2022137663-appb-100006
    步骤S160：根据所述6个图层图层判断该时间区间内的水稻种植分布以及水稻的复种指数。
  2. 根据权利要求1所述的水稻种植提取及复种指数监测方法，其特征在于，在步骤S110，输入同一地区时序MOD09GQ与MYD09GQ原始遥感影像的步骤中，所述MOD09GQ与MYD09GQ原始遥感影像的时间范围从第一年的10月份至第三年的3月份，时间总跨度1.5年，所述MOD09GQ与MYD09GQ遥感影像包括2个250米分辨率谱段，将这2个谱段命名为近红外谱段及红谱段。
  3. 根据权利要求2所述的水稻种植提取及复种指数监测方法，其特征在于，在步骤S120，对每天的MOD09GQ与MYD09GQ影像分别进行处理的步骤中，具体包括：
    对每天的MOD09GQ与MYD09GQ影像分别进行云覆盖检测，并将云覆盖像元进行掩膜处理；
    对云掩膜后的影像分别计算研究区域的归一化差分植被指数数据NDVI，计算公式如下：
    NDVI o/y＝(B nir-B red)/(B nir+B red)
    NDVI o、NDVI y分别对应MOD09GQ与MYD09GQ影像。
  4. 根据权利要求3所述的水稻种植提取及复种指数监测方法，其特征在于，在步骤S130：以8天为时间间隔对处理后的影像进行中值合成的步骤中，具体包括下述步骤：
    以8天为时间间隔对生成的NDVI时序进行中值合成，计算公式如下：
    Figure PCTCN2022137663-appb-100007
    其中，d表示合成的日期，d-3≤d 1,d 2≤d+4，median为中值算子。
  5. 根据权利要求4所述的水稻种植提取及复种指数监测方法，其特征在于，在步骤S140，对中值合成后的影像进行滤波，生成滤波后的时间序列的步骤中，具体包括下述步骤：
    对生成的时间序列NDVI d进行时间窗口为32，即{NDVI d+i*8}i＝-2,-1,0,1,2.的Savitzky-Golay滤波，生成滤波后的时间序列
    Figure PCTCN2022137663-appb-100008
  6. 根据权利要求5所述的水稻种植提取及复种指数监测方法，其特征在于，在步骤S150：对水稻种植的判别条件转变为单独的6个图层的步骤中，具体包括下述步骤：
    对滤波后的时间序列
    Figure PCTCN2022137663-appb-100009
    进行时间窗口50的局部极大值检测，按照
    Figure PCTCN2022137663-appb-100010
    的序列时间建立局部极大值时间序列图层
    Figure PCTCN2022137663-appb-100011
    计算公式如下：
    Figure PCTCN2022137663-appb-100012
    对原始的时间序列NDVI d进行前向40天，即前向6个时相{NDVI d-i*8}i＝5,4,3,2,1,0.的Mann-Kendall趋势检验，按照NDVI d的序列时间建立前向Mann-Kendall趋势检验图层
    Figure PCTCN2022137663-appb-100013
    计算公式如下：
    FMK＝Mann-Kendall{NDVI d-i*8},i＝5,4,3,2,1,0.
    Figure PCTCN2022137663-appb-100014
    对原始的时间序列NDVI d进行后向40天，即后向6个时相{NDVI d+i*8}i＝0,1,2,3,4,5.的Mann-Kendall趋势检验，按照NDVI d的序列时间建立后向Mann-Kendall趋势检验图层
    Figure PCTCN2022137663-appb-100015
    计算公式如下：
    BMK＝Mann-Kendall{NDVI d+i*8},i＝0,1,2,3,4,5.
    Figure PCTCN2022137663-appb-100016
    对滤波后的时间序列
    Figure PCTCN2022137663-appb-100017
    进行前向30-70天内的最小值计算及前向差异足够判别，建立前向NDVI差异判别图层
    Figure PCTCN2022137663-appb-100018
    计算公式如下：
    Figure PCTCN2022137663-appb-100019
    Figure PCTCN2022137663-appb-100020
    对滤波后的时间序列
    Figure PCTCN2022137663-appb-100021
    进行后向30-70天内的最小值计算及后向差异足够判别，建立后向NDVI差异判别图层
    Figure PCTCN2022137663-appb-100022
    计算公式如下：
    Figure PCTCN2022137663-appb-100023
    Figure PCTCN2022137663-appb-100024
    输入同一地区时序MOD09GA与MYD09GA原始遥感影像，时间范围从第一年的10月份至第三年的3月份，时间总跨度1.5年，MOD09GA与MYD09GA遥感影像包括7个500米分辨率谱段，对每天的MOD09GA与MYD09GA影像分别进行云覆盖检测，并将云覆盖像元进行掩膜处理；
    基于每天的MOD09GA与MYD09GA云掩膜数据，以及选择的纯净像元，应用中值平均计算城镇、水体、林地终端像元光谱值，分别标记为
    Figure PCTCN2022137663-appb-100025
    Figure PCTCN2022137663-appb-100026
    对MODIS MOD09GA与MYD09GA云掩膜数据，基于上步中的终端像元光谱值，进行混合像元分解Unmix，并建立水覆盖判别图层
    Figure PCTCN2022137663-appb-100027
    计算公式如下：
    Figure PCTCN2022137663-appb-100028
    Figure PCTCN2022137663-appb-100029
    Figure PCTCN2022137663-appb-100030
    基于水覆盖判别图层
    Figure PCTCN2022137663-appb-100031
    建立前向40-80天内水覆盖判别图层
    Figure PCTCN2022137663-appb-100032
    计算公式如下：
    Figure PCTCN2022137663-appb-100033
    基于上面各步骤中生成的局部极大值时间序列图层
    Figure PCTCN2022137663-appb-100034
    前向NDVI差异判别图层
    Figure PCTCN2022137663-appb-100035
    后向NDVI差异判别图层
    Figure PCTCN2022137663-appb-100036
    前向Mann-Kendall趋势检验图层
    Figure PCTCN2022137663-appb-100037
    后向Mann-Kendall趋势检验图层
    Figure PCTCN2022137663-appb-100038
    以及前向40-80天内水覆盖判别图层
    Figure PCTCN2022137663-appb-100039
    建立水稻种植判别图层
    Figure PCTCN2022137663-appb-100040
    计算公式如下：
    Figure PCTCN2022137663-appb-100041
    其中AND为按位与运算符，若图层
    Figure PCTCN2022137663-appb-100042
    像元值为1，则表示在日期d，该像元内种植水稻，且其NDVI达到整个物候期内的最大值。
  7. 根据权利要求6所述的水稻种植提取及复种指数监测方法，其特征在于，在步骤S160：根据所述6个图层图层判断该时间区间内的水稻种植分布以及水稻的复种指数的步骤中，具体包括下述步骤：
    根据图层
    Figure PCTCN2022137663-appb-100043
    设定日期区间的上下限进行累加，可以判断该时间区间内的水稻种植分布，如下式所示：
    Figure PCTCN2022137663-appb-100044
    Figure PCTCN2022137663-appb-100045
    若日期的区间设定为全年，则图层Layer Paddy表示该像元水稻的复种指数，即
    Figure PCTCN2022137663-appb-100046
  8. 一种水稻种植提取及复种指数监测系统，其特征在于，包括：
    数据采集单元：用于输入同一地区时序MOD09GQ与MYD09GQ原始遥感影像；
    数据处理单元：用于对每天的MOD09GQ与MYD09GQ影像分别进行处理；
    中值合成单元：用于以8天为时间间隔对处理后的影像进行中值合成；
    滤波单元：用于对中值合成后的影像进行滤波，生成滤波后的时间序列；
    判别单元：用于对水稻种植的判别条件转变为单独的6个图层，所述6个图层包括：局部极大值时间序列图层
    Figure PCTCN2022137663-appb-100047
    前向NDVI差异判别图层
    Figure PCTCN2022137663-appb-100048
    后向NDVI差异判别图层
    Figure PCTCN2022137663-appb-100049
    前向Mann-Kendall趋势检验图层
    Figure PCTCN2022137663-appb-100050
    后向Mann-Kendall趋势检验图层
    Figure PCTCN2022137663-appb-100051
    及前向40-80天内水覆盖判别图层
    Figure PCTCN2022137663-appb-100052
    输出单元：用于根据所述6个图层图层判断该时间区间内的水稻种植分布以及水稻的复种指数。
  9. 一种终端，其特征在于，包括：所述终端包括处理器、与所述处理器耦接的存储器，其中，
    所述存储器存储有用于实现权利要求1-7任一项所述的水稻种植提取及复 种指数监测方法的程序指令；
    所述处理器用于执行所述存储器存储的所述程序指令以控制水稻种植提取及复种指数监测。
  10. 一种存储介质，其特征在于，存储有处理器可运行的程序指令，所述程序指令用于执行权利要求1至7任一项所述水稻种植提取及复种指数监测方法。
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